{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree Classification Part 4"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:42:58.956Z",
          "iopub.execute_input": "2021-09-05T20:42:58.959Z",
          "shell.execute_reply": "2021-09-05T20:42:59.444Z",
          "iopub.status.idle": "2021-09-05T20:42:59.429Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:42:59.433Z",
          "iopub.execute_input": "2021-09-05T20:42:59.435Z",
          "iopub.status.idle": "2021-09-05T20:43:00.075Z",
          "shell.execute_reply": "2021-09-05T20:43:00.085Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Open_Close'] = (dataset['Open'] - dataset['Adj Close'])/dataset['Open']\n",
        "dataset['High_Low'] = (dataset['High'] - dataset['Low'])/dataset['Low']\n",
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume  Open_Close  \\\nDate                                                                   \n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200   -0.005025   \n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300   -0.029925   \n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100    0.002387   \n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700    0.011820   \n2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600    0.026190   \n\n            High_Low  Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                                                           \n2014-01-03  0.030928                  1                 1         1  0.012658  \n2014-01-06  0.047619                  1                 1         1  0.032500  \n2014-01-07  0.034063                  0                 1         0  0.012106  \n2014-01-08  0.028986                  0                 0         0  0.000000  \n2014-01-09  0.044444                  0                 0         1 -0.021531  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Open_Close</th>\n      <th>High_Low</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n      <td>-0.005025</td>\n      <td>0.030928</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n      <td>-0.029925</td>\n      <td>0.047619</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n      <td>0.002387</td>\n      <td>0.034063</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.012106</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n      <td>0.011820</td>\n      <td>0.028986</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>4.05</td>\n      <td>4.09</td>\n      <td>4.09</td>\n      <td>30667600</td>\n      <td>0.026190</td>\n      <td>0.044444</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.021531</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:43:00.079Z",
          "iopub.execute_input": "2021-09-05T20:43:00.081Z",
          "iopub.status.idle": "2021-09-05T20:43:00.105Z",
          "shell.execute_reply": "2021-09-05T20:43:00.127Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open', 'High', 'Low', 'Volume', 'Adj Close','Returns']].values\n",
        "y = dataset['Buy_Sell'].values"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:43:00.109Z",
          "iopub.execute_input": "2021-09-05T20:43:00.112Z",
          "iopub.status.idle": "2021-09-05T20:43:00.134Z",
          "shell.execute_reply": "2021-09-05T20:43:00.129Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Spilitting the dataset\n",
        "removed =[0,50,100]\n",
        "new_target = np.delete(y,removed)\n",
        "new_data = np.delete(X,removed, axis=0) "
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:43:00.138Z",
          "iopub.execute_input": "2021-09-05T20:43:00.141Z",
          "iopub.status.idle": "2021-09-05T20:43:00.146Z",
          "shell.execute_reply": "2021-09-05T20:43:00.160Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n",
        "clf = tree.DecisionTreeClassifier() \n",
        "clf=clf.fit(new_data,new_target) \n",
        "prediction = clf.predict(X[removed]) "
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:43:00.151Z",
          "iopub.execute_input": "2021-09-05T20:43:00.154Z",
          "shell.execute_reply": "2021-09-05T20:43:00.522Z",
          "iopub.status.idle": "2021-09-05T20:43:00.505Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Original Labels\",y[removed])\n",
        "print(\"Labels Predicted\",prediction)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Labels [1 1 0]\n",
            "Labels Predicted [0 0 1]\n"
          ]
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-05T20:43:00.524Z",
          "iopub.status.busy": "2021-09-05T20:43:00.510Z",
          "iopub.execute_input": "2021-09-05T20:43:00.512Z",
          "iopub.status.idle": "2021-09-05T20:43:00.518Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tree.plot_tree(clf) "
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
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            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:43:00.532Z",
          "iopub.execute_input": "2021-09-05T20:43:00.535Z",
          "iopub.status.idle": "2021-09-05T20:43:15.354Z",
          "shell.execute_reply": "2021-09-05T20:43:15.425Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}